System and method for assessing physiological signal quality

ABSTRACT

Systems and methods are provided for evaluating physiological signal quality. A physiological signal, based on a series measurements on a subject, may be received. A quality of the physiological signal received may be evaluated, and an analysis of the physiological signal may be based at least in part on the quality evaluation.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a 371 U.S. National Phase Entry ofPCT/US2018/013767, filed Jan. 16, 2018, which claims priority to U.S.Provisional Application No. 62/447,241, filed Jan. 17, 2017, andentitled “SYSTEM AND METHOD FOR ASSESSING PHYSIOLOGICAL SIGNAL QUALITY.”These applications are incorporated herein by reference as if set forthin their entireties for all purposes.

FIELD OF THE DISCLOSURE

This disclosure relates to the evaluation of the quality ofphysiological readings, and more specifically to the assessment ofphysiologic signals (such as electrocardiogram (ECG) signals) andvariable analysis of signals based on quality assessment.

BACKGROUND

Cardiovascular diseases (CVD) are the leading cause of death worldwide.Some studies note that approximately 30% of all deaths worldwide areassociated with CVD; in developing countries, CVD may bedisproportionally higher. The most common diagnostic method used todetect heart disease is measuring the heart's electrical activity byelectrocardiography (ECG). But not all ECG signals are of the samequality, and long-term ECG monitoring data often contain a variety ofartifacts (e.g., powerline interference, drift, impulse noise, andmuscle noise) that complicate subsequent analysis. The change in noiseintensity over time and overall non-stationarity of the signal can alsocomplicate the processing of the long-term signal. If signals areanalyzed without discrimination, or otherwise not effectively selectedbased on their quality or acceptability, the quality of the analysis(and potentially a diagnosis) could suffer. Moreover, if all signals areprocessed, even ones that lack usable readings, additional computingresources are used unnecessarily, something that is of particularconcern with portable devices or other applications with limitations inprocessing power. Real-time signal quality estimation can help withsuppressing false alarms, detecting sensor misplacement (andpotentially, making adjustments to device, sensor, or lead placement asneeded), selecting segments to extract clinically relevant features, ordefining parameters for further processing. Previous approaches toquality estimation have involved review of statistical or morphologicalfeatures from signals, such as review of successive QRS complexes, RRinterval lengths, arterial blood pressure, and ECG signal amplitudes.

Although ECG analysis is a well-accepted CV monitoring approach, its usehas been limited to in-clinic studies and Holter monitoring for discreteclinical studies. As consumer-grade and clinical-grade wearabletechnologies become more commonplace, the need for remote monitoring isalso likely to increase. A recent review of the field of consumer- andclinical-grade health monitors has noted that the market could grow to$20 billion by 2017. The value of wearable technologies is directlyrelated to the quality of meaningful findings they generate. Given therelatively limited processing power and data storage capacity ofwearable technologies, and the magnitude of the data the devicesgenerate, better evaluation of ECG signal quality would tend to make thedevices more useful and their results more reliable.

What is needed is a system and method for effectively evaluating thequality of the ECG signal prior to analysis and, based on signalquality, more intelligently route segments of the ECG signal fordifferent levels/types of additional analysis. Such a system would helpmitigate the need to analyze (all of the) massive amounts of datacollected by, for example, wearable devices, or any device in whichprocessing power might be limited or in which the quality of readingsreceived does not remain constant. Estimating quality before furtheranalysis can be especially useful to long-term recording by Holtermonitors and other experimental devices.

SUMMARY

The present disclosure provides exemplary systems and methods forestimating signal quality based on a quality metric such as a ratio ofthe noise-free signal power to noise power (“signal-to-noise ratio,” orSNR). A noise-free signal can be estimated using (for example) a WienerFilter, such as a Wavelet Wiener Filter (WWF). The SNR may be calculatedin either the time domain or frequency domain. In either domain, thecalculation may be performed in a sliding window of specified length,allowing for real-time processing. The systems and methods may alsoinvolve segmenting a physiological signal according to a quality metric,and subsequently applying different processing methods to individualsegments. In this way, complicated analysis of poor-quality data can beavoided, and data can be processed using algorithms that are tuned tothe signal quality. Further advantages and features of the inventionwill be apparent from the remainder of this document in conjunction withthe associated drawings.

In accordance with one aspect of the disclosure, a method is providedfor evaluating signal quality. The method includes receiving aphysiological signal based on a series measurements on a subject,evaluating a quality of the physiological signal received, and, basingan analysis of the physiological signal at least in part on the qualityevaluation.

In accordance with another aspect of the disclosure, a system isprovided for evaluating electrocardiographic (ECG) signal quality. Thesystem includes a processor executing instructions that cause theprocessor to receive a physiological signal based on a seriesmeasurements on a subject, evaluate a quality of the physiologicalsignal received, and base an analysis of the physiological signal atleast in part on the quality evaluation.

In accordance with another aspect of the disclosure, a method isprovided for evaluating electrocardiographic (ECG) signal quality. Themethod includes receiving ECG signals based on readings of electricalactivity in the heart of a subject, determining an ECG signal power forthe ECG signals received, and calculating a quality metric based on thepower of the ECG signals received.

In accordance with another aspect of the disclosure, a system isprovided for evaluating electrocardiographic (ECG) signal quality. Thesystem includes a processor executing instructions for receiving ECGsignals based on readings of electrical activity in the heart of asubject, determining an ECG signal power for the ECG signals received,and calculating a quality metric based on the power of the ECG signalsreceived.

In accordance with another aspect of the disclosure, a method isprovided for evaluating signal quality in real time. The method includesreceiving ECG signals based on readings of electrical activity in theheart of a subject, determining a quality metric for the ECG signalsreceived, and basing analysis of the ECG signals on changes in thequality metric.

In accordance with another aspect of the disclosure, a method isprovided for evaluating signal quality in real time. The method includesusing an ECG device to measure electrical activity in the heart of asubject to obtain ECG signals, calculating a quality metric for the ECGsignals, and determining an analysis level based on the quality metric.

In accordance with another aspect of the disclosure, a system isprovided for evaluating signal quality in real time. The system includesan ECG device for measuring electrical activity in the heart of asubject and providing ECG signals and a processor configured tocalculate a quality metric for the ECG signals and determine an analysislevel based on the quality metric in real time.

Other aspects of the disclosure will be made apparent from thedisclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary signal quality estimationprocess using a sliding window approach.

FIG. 2 is a block diagram of an exemplary signal quality estimationprocess using a short time Fourier transform (STFT) frequency approach.

FIG. 3 is a block diagram showing an exemplary signal processing processfor segments of varying quality.

FIG. 4 shows five significant points delineated on an artificial ECGcurve used for validation: P onset, P offset, QRS onset, QRS offset, andT offset.

FIG. 5A shows, an artificial ECG signal with changing noise level andestimates of noise-free signal by wavelet Wiener filter (WWF) correlatedwith a comparison for signal-to-noise ratio (SNR) curve calculation inthe time domain and frequency domain, without (dashed line) and with(solid line) LP filtering.

FIG. 5B shows a spectrogram of estimated noise-free signal calculated bythe WWF correlated with a spectrogram of noise

FIG. 6A provides a graph that shows that sensitivity (Se) and positivepredictive value (P+) depend on SNR for QRS complex detection.

FIG. 6B provides a graph that shows that the standard deviation (STD) ofdifferences depends on SNR for other significant points.

FIG. 7 shows, at the top, an artificial ECG signal with detected segmentborders and a continuous SNR curve, two determined thresholds, and finalsegment distribution.

FIG. 8 shows provides correlated graphs of an ECG signal taken from theshoulder, a continuous SNR curve, and three sub-regions of an analyzedECG.

DETAILED DISCUSSION

Defining a quality metric is useful in evaluating the quality of aphysiological signal (i.e., a series of measurements) acquired via, forexample, electrocardiography (ECG), electromyography (EMG), bloodoxygenation level dependent (BOLD) imaging in functional magneticresonance imaging (fMRI), photoplethysmography (PPG), and the like. Toclassify quality, a quality metric such the signal-to-noise ratio (SNR)of the signal itself can be estimated using an estimate of thenoise-free signal. This quality metric allows for determination of theaccuracy or value of physiological signal analysis at various qualitylevels (e.g., with respect to ECG signals analysis, the ability toidentify a QRS complex given a particular signal SNR). In the ECGcontext, the system can indicate, for example, when (a relatively morecomputationally-demanding) complete full wave analysis versus (arelatively less computationally-demanding) QRS detection are feasiblebased on the SNR signal. It is noted that exemplary ECG signal analysesare provided below and in the drawings as non-limiting applications ofthe exemplary systems and methods discussed in the present disclosure.

In estimating SNR, the examined digital signal x[n] can be treated as anadditive mixture of noise-free signal s[n] and noise w[n], according tothe equation x[n]=s[n]+w[n], where n represents the digital timesequence. Improving the estimation of the noise-free signal improves thecalculation of SNR. To estimate the noise-free signal, a Wavelet WienerFilter method (WWF)—a two-stage algorithm operating in the waveletdomain—can be used. In the first stage of the WWF, thresholding of thewavelet coefficients is used to estimate the noise-free signalcoefficients u_(m)[n]. The details of threshold selection andreconstruction are described in L. Smital, M. Vítek, J. i. Kozumplík,and I. Provaznik, “Adaptive wavelet Wiener filtering of ECG signals,”Biomedical Engineering, IEEE Transactions on, vol. 60, pp. 437-445,2013.

In the second stage, a Wiener correction factor g_(m)[n] is computedaccording to equation (1), and the input (noisy) coefficients y_(m)[n]are adjusted according to equation (2):

$\begin{matrix}{{{g_{m}(n)} = \frac{u_{m}^{2}(n)}{{u_{m}^{2}(n)} + {\sigma_{m}^{2}(n)}}},} & (1) \\{{{{\overset{\sim}{y}}_{m}(n)} = {{y_{m}(n)}{g_{m}(n)}}},} & (2)\end{matrix}$where σ² _(m)[n] is the variance of the noise coefficients in the m-thfrequency band and {tilde over (y)}_(m)[n] is the estimation of thedenoised wavelet coefficients. After processing is completed in thewavelet domain, the final result can be transformed into the time domainto determine the denoised output signal.

By subtracting the noise-free signal estimate from the input signal, theestimated noise in the input signal can be computed. To compute thelocal SNR signal, one of at least two approaches can be used for SNRestimation after separating the noise and signal components: 1) atime-based sliding window approach; and 2) a short time Fouriertransform (STFT) frequency approach.

One non-limiting example of an implementation of the sliding windowapproach is illustrated in the block diagram in FIG. 1, which usestime-domain processing. A high pass filter 100 used for initialprocessing is added to remove the components of the input signal offrequencies below 0.67 Hz (primarily those attributed to baselinewander, movement, and respiration), for example, which can influence theaccuracy of SNR calculation. As described above, a WWF block 102 can beincluded followed by an SNR block 104. In the SNR block 104, the localenergy of the noise-free signal s[n] and noise w[n] bounded by windows(W) with a length of two seconds can be computed. The SNR estimated bythe sliding window approach (SNR_(win)) is computed in decibelsaccording to equation (3):

$\begin{matrix}{{{SNR}_{win}\lbrack n\rbrack} = {10\;{\log_{10}\left( \frac{\sum\limits_{j = {n - {W/2}}}^{n + {W/2}}\left( {s\lbrack j\rbrack} \right)^{2}}{\sum\limits_{j = {n - {W/2}}}^{n + {W/2}}\left( {w\lbrack j\rbrack} \right)^{2}} \right)}\mspace{14mu}{{dB}.}}} & (3)\end{matrix}$

A low pass filter block 106 can be included before the SNR_(win) isdelivered.

Referring to FIG. 2, second non-limiting approach for estimating SNR isbased on the STFT, is provided. Again the input signal, x, can be fed toa WWF block 200 to deliver the noise-free signal, s, and the noisesignal, w, to respective STFT block 202. The STFT blocks 202 estimatethe local frequency content and creates a matrix representing thedevelopment of the spectrum over time, which will be called thespectrogram (SG).

The noise power and the noise-free signal is computed in block SNR 204,such that the noise power and the noise-free signal are estimated fromthe area in the spectrograms between frequencies f₁ (e.g., 0.67 Hz) andf₂=f_(s)/2, where f_(s) denotes the sampling frequency. This frequencyrange covers the dominant frequency content of the ECG signal and musclenoise, the most common broadband interference to corrupt the ECG signal.A floating window (W) with a length of two seconds can be used for powerestimation. The SNR estimated by the STFT approach (SNR_(STFT)) iscomputed using equation (4):

$\begin{matrix}{{{{SNR}_{STFT}\lbrack n\rbrack} = {10\;{\log_{10}\left( \frac{\sum\limits_{i = f_{1}}^{f_{2}}{\sum\limits_{j = {n - {W/2}}}^{n + {W/2}}\left( {{SG}_{s}\left\lbrack {i,j} \right\rbrack} \right)^{2}}}{\sum\limits_{i = f_{1}}^{f_{2}}{\sum\limits_{j = {n - {W/2}}}^{n + {W/2}}\left( {{SG}_{w}\left\lbrack {i,j} \right\rbrack} \right)^{2}}} \right)}{\mspace{11mu}\;}{dB}}},} & (4)\end{matrix}$where SG_(s) and SG_(w) are the spectrograms of the noise-free signaland the noise, respectively.

Due to the fixed window size of W, the estimate of SNR is variableaccording to the number of QRS complexes within the window. Accordingly,for both approaches the SNR estimate is averaged afterwards, in LPblock, 106, 206, in another window of the same length.

By measuring the quality of the ECG signal continuously, it can bedetermined which segments of the signal are suitable for furtherprocessing and which are not. However, before the signal is divided intodifferent segments according to quality, the quality classes of interestare defined. A suitable definition of quality classes may be, as anon-limiting example: (Q1) segments that exhibit low noise levels andallow full ECG wave analysis; (Q2) segments that contain higher levelsof noise (than Q1) but can be processed reliably for QRS detection; and(Q3) segments that contain exceedingly high levels of noise thatpreclude further meaningful processing.

FIG. 3 illustrates a signal processing pipeline that can be used todetermine ECG signal segments of varying quality. The input signal, x,is fed into an SNR_(est) block 300, which represents the SNR estimationalgorithm shown above in FIGS. 1 and 2. This feeds into a THR block 302that tracks the continuous SNR curve and produces classified labels usedfor segmentation of the input signal in an SEG block 304, wheresegmentation proceeds according to the received labels. Individualsegments of the input signal are further processed according to theirquality.

Specifically, quantifying (in decibels of SNR) the differences inthreshold levels between the quality classes is important to the processof defining Q1, Q2, and Q3. A full ECG waveform analysis block 306receives the Q1 segments allow reliable detection of the QRS complex andfive other significant points in the ECG curve, as illustrated in FIG.4. A QRS block 308 is designed to deliver reliable detection of the QRSsegment, which satisfies the criteria for quality class Q2. QRSdetection is considered successful if the sensitivity and positivepredictive value are greater than a threshold, such as 99.5%. Detectionaccuracy below the threshold can be used as a differentiator for a DISCblock 310 that selects the quality class Q3. These thresholds betweenquality classes were determined using an artificial model of an ECGsignal and artificial EMG noise, as further discussed below. Thethresholds can be adjusted and varied as desired to suit particularapplications.

The signal quality estimate can be used to delineate the three classes.An ECG segmentation algorithm (e.g. the process of labeling a datasetwith Q1, Q2, and Q3) can include two steps: basic segmentation andcorrection. The basic segmentation can naively label temporal segmentssolely on the basis of the thresholds selected for the Q1, Q2, and Q3classes. These thresholds mark the end of one segment and the beginningof another, and because the SNR level can change rapidly (particularlyduring motion), the length of each labeled section may be quitevariable. Accordingly, correction rules may be applied to retain onlythose segments that are suitable for subsequent analysis.

There are at least two separate sets of correction rules that may beemployed: for example, a first for when a generally high SNR signalfalls within an otherwise low SNR signal segment, and a second for whena generally low SNR signal falls within an otherwise high SNR signalsegment. In the first case, when a high SNR signal is, for example,shorter than 15 seconds, or its average SNR value is, for example,within one decibel (dB) of the label threshold, the segment can beignored (i.e., labeled as low SNR similar to adjacent segments). Incontrast, for a low SNR segment that is, for example, shorter than twoseconds, or where the average SNR value is within, for example, one dBof the label threshold, the low SNR signal is treated as if it were ahigh SNR signal. This sort of correction can be used to eliminate shortsegments of signals that are higher in quality than neighboring segmentsbut which are nonetheless not considered suitable for further analysis.Conversely, such correction can be used to eliminate short segments ofsignals that are of lower quality than neighboring segments, but whichare deemed acceptable or desirable for analysis.

Two different ECG datasets have been used to test the algorithms. Thefirst is an artificial ECG dataset for which the required noise levelcan be set. The second is a dataset acquired from an IRB-approvedvolunteer study that used a specially designed wearable ECG device. Forthe artificial ECG dataset, an artificial model of a noise-free signalwas corrupted with artificial noise. As a model of an ECG signal, onecycle of a filtered real ECG signal from the “Common Standards forQuantitative Electrocardiography” database—which contains very highquality waveforms with little noise (e.g., see FIG. 4)—was used. Byrepeating this cycle, an artificial noise-free ECG signal of variablelength was obtained. In the design of this model, the approaches usedwere validated in the literature, in which the typical power spectraldensity of surface muscle noise is described. The artificial noise wasgenerated by filtering white Gaussian noise through a digital filterwith frequency characteristics similar to the power spectra of musclenoise. The final signal-to-noise ratio was obtained by adjusting theamplitude of the noise, as described by Smital et al. (cited above).

For the dataset acquired from a volunteer study, a custom physiologicmonitoring platform was used to collect high-resolution ECG data duringactivity. The platform includes a one-lead configuration for highresolution ECG data collection. The device incorporated a custom lowpower (100 μW at a 2.8 V supply voltage) ECG circuit with 100 dB commonmode rejection. In addition, to capture posture and physical activity,each device also contained 2 g (VTI Technologies, CMA3000-A01) and 16 gtri-axial accelerometers (Analog Devices, ADXL326BCPZ). The device dataacquisition rates for both ECG and motion are programmable, and were setto 400 samples per second for the ECG monitor and 10 samples per secondper individual x-, y-, and z-axis, respectively. This deviceconfiguration allows continuous recording for 14 days on a single 750mAH battery (Bi-power, BL-7PN-S2).

The artificial model of the ECG signal and the artificial EMG noisediscussed above can be useful in illustrating the functionality of SNRestimators. EMG noise can be generated with a gradually changing level,as demonstrated in at graph 500 FIG. 5A. The graph 500 of FIG. 5A showsan artificial ECG signal with changing noise level 502 and estimates ofnoise-free signal by wavelet Wiener filter (WWF) 504 This graph 500 iscorrelated with a second graph 506 providing a comparison forsignal-to-noise ratio (SNR) curve calculation in the time domain 508 andfrequency domain 510, without (dashed line) 512 and with (solid line)514 LP filtering. In FIG. 5A, three noise levels consistent with noiseobserved in real data were chosen: −7.5 dB for large interference, 7.5dB for moderate interference, and 22.5 dB for low interference.

FIG. 5B shows spectrograms used for SNR estimation by the STFT frequencyapproach. The rectangles 516, 518 indicates the two-second floatingwindow used for calculating signal power. The spectrogram of thenoise-free signal in the top panel shows no significant change overtime, as it is an estimate of an artificial ECG. In the bottom panel ofFIG. 5B, a plot of the residual noise spectrogram shows two clearchanges of energy corresponding to the level of generated noise thatcauses a step change in the estimated SNR curve. The bottom panel ofFIG. 5A shows the true noise level, as well as results of bothestimation methods.

As shown in the bottom panel of FIG. 5A, there is a cyclic,low-amplitude component to the SNR signal. Variability in the SNRestimation is related to the fixed window size W (=2 sec) and a variablenumber of QRS complexes within the window. This variability can beremoved by LP filtration at the end of the processing chain, as shown inthe bottom panel of FIG. 5A. As can be seen, both estimators yieldapproximately equal results, and both are similar to the preset noiselevel. Estimates of the SNR vary most from the true SNR at theartificial transitions between SNR levels. SNR can be better estimatedfor rapidly changing SNR levels by dynamically adjusting the window sizeaccording to features within the data.

QRS complex detection and ECG delineation were used to search for thethresholds (in decibels of SNR) between different quality classes. Totest the ability of a software implementation to detect these boundariesin an artificial ECG dataset, artificial muscle noise was added. In thistesting procedure, the SNR was gradually increased in the artificialmixture, and six significant points in the ECG waveform were sought. Foreach step of SNR, an artificial mixture with 104 RR intervals wasgenerated. In equation (5) below, detection accuracy is expressed by thesensitivity (Se) and positive predictive value (P+) for the QRS complexposition and by the standard deviation (STD) of the differences (betweenreference and detected positions) for other significant points. Thesestatistical parameters are defined respectively as:

$\begin{matrix}{{{Se} = \frac{TP}{{TP} + {FN}}},{P^{+} = \frac{TP}{{TP} + {FP}}},} & (5)\end{matrix}$where TP represents true positive values (correctly detected points), FNrepresents false negative values (undetected points), and FP representsfalse positive values (incorrectly detected points). The detectedposition is identified as TP when there is a reference position withinthe 50 ms tolerance window. (Certain tolerance windows normally used forQRS detection were obtained from Z. Zidelmal, A. Amirou, D.Ould-Abdeslam, A. Moukadem, and A. Dieterlen, “QRS detection usingS-Transform and Shannon energy,” Computer methods and programs inbiomedicine, vol. 116, pp. 1-9, 2014.) FIGS. 6A and 6B shows the curveobtained by plotting these statistics according to the SNR of theartificial mixture.

As these curves demonstrate, the detection accuracy of all of the pointsincreases as the noise level in the signal decreases. As noted above,detection of the QRS complex is considered sufficient in this example ifboth Se and P+ are greater than 99.5%. Both of these conditions aresatisfied if the ratio of the power of the SNR is greater than 0.32 dB.Accurate delineation of other significant points in the ECG waveform ismore sensitive to the quantity of noise. The detection of significantpoints is considered successful if the standard deviation of differencesbetween the reference and detected positions is less than the criteriaspecified in Table I (below). In this scenario, satisfying all of theconditions in Table I is possible only if the SNR is greater than 15.35dB, as illustrated in FIG. 6B.

TABLE I CRITERIA 2sCSE FOR STANDARD DEVIATION OF DETECTION ERROR P_(on)P_(off) QRS_(on) QRS_(off) T_(off) 10.2 ms 12.7 ms 6.5 ms 11.6 ms 30.6

In this implementation, ECG signals containing more than 0.32 dB ofnoise are considered of insufficient quality for further processing(e.g. Q3). If the signal SNR is between 0.32 and 15.35 dB, it is deemedthat only reliable QRS detection is possible (e.g. Q2). Full waveanalysis is considered desirable if the noise level in the signal isless than 15.35 dB (e.g. Q1). These thresholds can subsequently beapplied during processing in the THR block (see FIG. 3).

To illustrate the functionality of such a segmentation scheme, anartificial ECG signal was created, with various levels of noise added.The result of segmentation is shown in FIG. 7. In the upper graph ofFIG. 7, an artificial ECG signal with detected segment borders (verticallines). In the lower graph of FIG. 7, a continuous SNR curve 700, twodetermined thresholds 702, and final segment distribution 704 areillustrated. In addition to delineating segments of Q1, Q2, and Q3, thecorrection step excluded a segment of high SNR signal during the initialQ3 segment. Although the quality of this segment is satisfactory, itsshort-time length precludes further analysis. The short “low-SNR” areabetween segments Q1 and Q2 was correctly segmented, and thus would notinfluence full wave analysis in Q1 or QRS detection in Q2. Separatelyapplying different rules to the high-quality and degraded segments isuseful to retain only those sections that are suitable for subsequentanalysis.

These methods for SNR calculation and ECG segmentation have beenverified using real electrocardiogram data acquired by a wearablephysiological monitor. FIG. 8 provides experimental results illustratingan ECG dataset that was recorded as a one-lead signal from the shoulderof an informed-consent subject. The upper panel 800 of FIG. 8 shows 28minutes of ECG signal recording. During this time the subject was askedto perform different activities. The subject was in the supine positionfor the first 7.5 minutes, then assumed a sitting position until the 16minute mark, and a standing position until the 24 minute mark; thesubject then performed twenty squats, and at the 25.5 minute markreturned to the supine position. The involvement of different musclegroups in these activities produced different amounts of noise. Theamount of noise measured by the continuous SNR curve 802 can be observedin the middle panel 804 of FIG. 8. Block THR (see FIG. 3) is responsiblefor tracking this noise activity; if the SNR was found to significantlycross one of the thresholds 806 a new segment was initialized, asevident by the change in the level of the tracking line 808. The bordersof new segments are marked by vertical color lines in the top panel ofFIG. 8. As presented in the lower panel of FIG. 8, new segments areinitiated when the noise level changes significantly. Even when thenoise level is highly variable throughout the experiment, both detectors(QRS and full wave) are able to annotate the data. The bottom panel ofFIG. 8 shows three zoomed-in sub-regions of an analyzed ECG.

An objective evaluation of the segmentation algorithm was alsoperformed. Before starting the automatic segmentation, time points weremanually identified to define the beginning of new segments, accordingto the SNR curve. The automatically determined distribution of qualitysegments agreed with the assumptions made here to within 99.02%.

Correct classification of the determined segments was verified by theresults of QRS complex detection, and by delineation of othersignificant points within the corresponding segments. The upper panel ofFIG. 8 displays the results of QRS detection performed on segmentsidentified as Q2 (“medium” quality); the numbers represent sensitivityand positive predictive value. This result satisfies the assumption forreliable QRS detection as outlined by Köhler et al. in “The principlesof software QRS detection,” Engineering in Medicine and BiologyMagazine, IEEE, vol. 21, pp. 42-57, 2002. In segments identified as Q1(deemed to be of the highest relative quality), full wave analysis wasperformed. For these segments, the standard deviations of detectionerror (compared to manual annotation) for five significant points areshown in the top panel of FIG. 8. In most cases, the data satisfied thecriteria in Table I; those criteria not met are marked in red.

Data analysis preferably begins with a pre-processing step tocharacterize data quality, which may be impacted by such factors asimproper attachment, sensor failure, and physiologic noise. Exemplaryversions of the approaches discussed above need rely only on, and canadapt to, the characteristics of the input signal. Advantageously, theseapproaches provide the ability to quantitatively select subsequentsignal analysis based on the SNR through well-defined limits ofdetection. The decision rules used can be tailored to target particularwaves in the ECG signals depending on SNR. For example, full ECG waveanalysis is robust for signals with an estimated SNR of more than 15.35dB. Because of the small amplitude of P waves, detecting their borderscan be challenging. Accordingly, in Q1 segments when the SNR wasestimated near 20 dB, the SNR locally measured in the area of the Pwaves was close to zero dB. Decision rules may be adjusted so as to, forexample, specifically target T wave detection at lower SNR, and P wavedetection at higher SNR.

There are many clinical and non-clinical health reasons to monitor ECGsignals. To do so on a wearable platform is challenging due to thelimited available computational, memory, and battery resources.Moreover, the quality of data obtained from a wearable device may varygreatly depending on the sensor, sensor placement, and activity of theperson wearing the device. In order to appropriately process the ECGsignal and report meaningful findings—e.g. to assess the diagnosticperformance across multiple devices or bounds of signal quality in largeclinical trial protocol design—it is important to characterize thequality of the signal prior to analysis. The approaches discussed aboveto estimating the SNR of the signal, in combination with decision rulesfor selecting appropriate analysis protocols, can facilitate on-boardembedded analysis of ECG signals by, for example, small microcontrollersor microprocessors executing the computer code implementation of thesealgorithms.

Therefore, systems and methods are provided to estimateelectrocardiogram signal quality using a local time window by continuouscalculation of the ratio between the noise-free signal power and noisepower (SNR). This signal quality metric allows not only binarydetermination between good/bad signals, but also quantification of thequality of specific segments of the signal. The system determines thesignal SNR by first estimating a noise-free signal and comparing it tothe residual noise component. Following SNR estimation, the signal canbe segmented into bins according to the quality level of the data. Forexample, signals can be quantitatively distinguished according two ormore levels of signal quality as indicated by the SNR estimate, such as:signals that contains very low noise levels and are suitable for fullECG wave analysis; signals containing a moderate level of noise thatstill allow reliable ECG QRS detection; and signals that contain anexcessive amount of noise, making that segment undesirable for furtherprocessing. The system can be used with readings from any device takingreadings, including low-power mobile and wearable devices which may haverelatively limited computing power. The system provides distinctadvantages for pipelining large amounts of data collected by (forexample) wearable devices into tailored data analysis by accounting forhigh-priority ECG signal features. Additionally, the system facilitatesquantification of the maximum possible or allowable noise for reliableQRS detection and delineation of the full ECG waveform. QRS (mediumquality) and full ECG (high quality) waveform detection limits inpreferred implementations may be set to, for example, 0.32 and 15.35 dB,respectively.

The present invention has been described in terms of one or morepreferred embodiments, and it should be appreciated that manyequivalents, alternatives, variations, additions, and modifications,aside from those expressly stated, and apart from combining thedifferent features of the foregoing versions in varying ways, can bemade and are within the scope of the invention. Following are additionalexamples. These examples are not to be construed as describing the onlyadditions and modifications to the invention, and the true scope of theinvention will be defined by the claims included in any later-filedutility patent application claiming priority from this provisionalpatent application.

For example, as suggested above, although the discussion has focused onECGs, alternative versions of the invention can be used to evaluateother physiological signals, such as signals resulting fromelectroencephalography (EEG) and magnetoencephalography (MEG). Also,although certain advantages have been discussed in particularimplementations, such as with wearable ECG devices, the benefits of theinvention can be realized in other devices as well, such as high-qualitylaboratory and clinical equipment, or other devices receiving signalswith signal quality levels that potentially vary over time or that areto be monitored. Moreover, although the quality metric discussed aboveis SNR, other metrics based on signal power, noise power, and/or otherreadings may also be used. Further, although the discussion abovefocused on three quality levels, any number of quality levels arepermitted, with each quality level deemed suitable for differentanalyses. Furthermore, the above systems and methods can be implementedusing hardware, software, single integrated devices, multiple devices inwired or wireless communication, or any combination thereof.

In various exemplary versions, any signal (i.e., series of measurements)may be taken, and the signal quality estimated by determining a signalquality metric. Based at least in part on the signal quality metric, adecision system may be implemented to make a decision about a subsequentstep in the processing/analysis of the signal. The signal may be, forexample, any time-varying physiologic signal, examples of which areprovided above. In the first step (i.e., estimating signal quality), anestimate of the noise-free signal may be calculated. In the ECG context,the noise-free signal may be estimated by filtering the ECG signal, asdiscussed above. However, there are alternative approaches.

In certain configurations, the noise-free signal may be based on astimulus/interrogation signal. For example, in the context of BOLDimaging, the brain may be stimulated with a known temporal pattern ofvisual (or other sense) stimuli. The signal is recorded, providing aninput temporal pattern which can be used to estimate the noise-freesignal that is then used to estimate the signal quality. In alternativeversions, a noise free signal may be estimated using an availablestatistical model. For example, statistical models of the cardiovascularand pulmonary components in a PPG signal may be used; such separatestatistical models (e.g., rate of variation, occurrence of signal peaks,etc.) can serve as the noise-free signal.

Alternative configurations may use non-temporal equivalents forestimation of the noise-free signal. For example, a prior statisticalmodel of the shape of a tumor may be available. A patient can be scannedusing, for example, magnetic resonance (MR), computerized tomography(CT), nuclear medicine (NM), etc. Before using such image data, thequality of the image (which is spatially varying, not temporallyvarying) may be estimated. The prior statistical model of the shape ofthe tumor may be used to determine if the imaging has enough signalquality to perform a segmentation of the image.

One example for such an approach involves ultrasound image acquisition.For example, a two-dimensional (2D) B-Mode ultrasound may be used toimage a Field of View (a tumor and surrounding vascular structures).Multiple 2D ultrasound images may be used to create a three-dimensional(3D) model. Questions that may arise include, for example, whether thereis a sufficient number of 2D images for calculating the volume of thetumor, or whether there are enough images and spatial resolution tosegment out the vessel bed. Exemplary systems and methods could use aprior model (such as a segmented CT scan) to determine the quality ofthe dataset and answer such questions independently.

Regarding the decision system, different algorithms may be used to makea decision about an upcoming processing/analysis step. In the context ofECGs, for example, the decision may involve R-R interval analysis and/orfull wave analysis. In the context of PPGs, respiration rate, bloodoxygenation computation, and/or heart rate computation, for example, maybe used to make a decision. For ultrasound imaging, the decision mayinvolve segmentation, volume estimation, and/or texture measurement, forexample. The decision system may use the signal quality estimation todetermine which analysis algorithms are appropriate given the inputsignal. For some PPG signals, for example, it may be possible to obtainrespiration and blood oxygenation data, but not heart rate. For anultrasound system, it may be sufficient to perform volume estimation butnot segmentation. The decision system may base this decision on theestimate of the noise-free signal and the estimate of the signalquality.

By way of approximate, non-limiting analogy with respect to certainexemplary versions, the approach may be said to have certain conceptualsimilarities to the negotiation of baud rates by certain modems. Onemodem may send a known signal to the other modem, and the other modemmay respond back with an estimate of what it heard; this exchange mayrepeat until an optimal baud rate is chosen. By way of distinction fromthe modem analogy, however, a signal input may not be needed because thesignal itself may be used for determining the signal quality. Also,while the modems are attempting to evaluate the connection between eachother, it is not the connection between two points (i.e., thephysiologic signal and the sensor) that are being optimized here.Moreover, while the modem aims to provide communication between two endpoints, here, based on the estimate of signal quality, a choice is madefrom a collection of different algorithms that yield different results.As another non-limiting analogy with respect to certain exemplaryversions, the process may be said to have certain conceptualsimilarities to a smartphone connecting to a wireless tower. The phonemay estimate the signal quality between it and the tower, and based onthe quality, the phone may decide, for example, that it can only be usedfor analog phone calls and text messages, or that it can be used fortext-only internet requests and digital calls.

We claim:
 1. A method for evaluating signal quality, the methodincluding the steps of: a. receiving, by a processor, a physiologicalsignal based on a series of measurements on a subject; b. evaluating, bythe processor, a quality of the physiological signal received comprisingestimating a noise-free signal power and estimating a noise power forthe series of measurements; and c. basing, by the processor, an analysisof the physiological signal at least in part on the quality evaluationcomprising evaluating the signal quality of the series of measurementsbased on the estimated noise-free signal power and the estimated noisepower.
 2. The method of claim 1 wherein evaluating a quality of thephysiological signal includes estimating a signal quality metric.
 3. Themethod of claim 2 wherein the quality metric is a signal-to-noise ratioof the noise-free signal power to the noise power.
 4. The method ofclaim 2 wherein the noise-free signal is estimated using astimulus/interrogation signal.
 5. The method of claim 4 wherein thephysiological signal is a blood oxygenation level dependent (BOLD)imaging signal, and the stimulus/interrogation signal is a neuralstimulation signal.
 6. The method of claim 5 wherein the neuralstimulation signal is a temporal pattern of a sense stimulus.
 7. Themethod of claim 5 wherein the neural stimulation is a known temporalpattern of a visual stimulus.
 8. The method of claim 7 further includingthe step of recording the neural stimulation to estimate the noise-freesignal.
 9. The method of claim 2 wherein estimating a signal qualitymetric includes using a statistical model as a noise-free signal. 10.The method of claim 9 wherein the statistical model corresponds with oneor more of a rate of variation and an occurrence of signal peaks. 11.The method of claim 9 wherein the statistical model corresponds with thecardiovascular and pulmonary components in a photoplethysmography (PPG)signal.
 12. The method of claim 1 wherein the noise-free signal power isestimated using one or more filters.
 13. The method of claim 12 whereinthe one or more filters includes a Wavelet Wiener Filter.
 14. The methodof claim 1 wherein basing an analysis of the physiological signalincludes selecting between two analyses with different computationaldemands.
 15. The method of claim 1 wherein basing an analysis of thephysiological signal includes selecting an analysis algorithm.
 16. Themethod of claim 15 wherein the physiological signal is anelectrocardiography (ECG) signal, and the analysis algorithm includes atleast one of an R-R interval analysis and a full-wave analysis.
 17. Themethod of claim 15 wherein the physiological signal is aphotoplethysmography (PPG) signal, and wherein the analysis algorithm isbased at least in part on one or more of a respiration rate, a bloodoxygenation computation, and a heart rate computation.
 18. The method ofclaim 15 wherein the physiological signal is an ultrasound imagingsignal, and wherein the analysis algorithm is based at least in part onone or more of a segmentation, a volume estimation, and a texturemeasurement.
 19. The method of claim 1 wherein physiological signal istime-varying.
 20. The method of claim 1 wherein physiological signal isspatially-varying.